IMPROVING PROTEIN IDENTIFICATION SENSITIVITY BY COMBINING MS AND MS/MS

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 我们利用LTQ-Orbitrap高质量精度数据，结合MS和MS/MS信息，研究和比较了三种猎枪蛋白质鉴定方法。在第一种方法中，我们使用了唯一的质量识别符方法，其中与从MS/MS数据库搜索中识别的蛋白质预测的肽匹配的MS峰首先被减去，然后使用MS峰作为蛋白质鉴定的唯一质量识别符。在第二种方法中，我们使用了一种精确的质量和时间标签方法，通过建立以前MudPIT分析的潜在质量和保留时间数据库。对于第三种方法，我们使用了一种类似于肽质量指纹图谱的方法，并结合随机数据库进行蛋白质鉴定。结果表明，结合MS和MS/MS信息可以提高对低丰度蛋白质的识别灵敏度。此外，MS/MS数据库搜索的“一键奇观”可以进一步得到MS信息的证实，该方法改进了低丰度蛋白质的鉴定。然后讨论了这三种方法的优缺点。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. We investigated and compared three approaches for shotgun protein identification by combining MS and MS/MS information using LTQ-Orbitrap high mass accuracy data. In the first approach, we employed a unique mass identifier method where MS peaks matched to peptides predicted from proteins identified from an MS/MS database search are first subtracted before using the MS peaks as unique mass identifiers for protein identification. In the second method, we used an accurate mass and time tag method by building a potential mass and retention time database from previous MudPIT analyses. For the third method, we used a peptide mass fingerprinting-like approach in combination with a randomized database for protein identification. We show that we can improve protein identification sensitivity for low-abundance proteins by combining MS and MS/MS information. Furthermore, "one-hit wonders" from MS/MS database searching can be further substantiated by MS information and the approach improves the identification of low-abundance proteins. The advantages and disadvantages for the three approaches are then discussed.